Aspect-Based Sentiment Analysis with Deep Learning: A Multidomain and Multitask Approach

Abstract

Sentiment analysis aids in obtaining the opinion of the users towards a particular product, service or policy. Focusing on classifying the sentiment that corresponds to each aspect of the entity in the document will help to identify the sentiment more clearly. This is also the mission of aspect-based sentiment analysis (ABSA). The vast majority of prior studies in ABSA have implemented single-task execution models on single-domain datasets. This is inconvenient when it is necessary to perform the full range of tasks in ABSA and on domain-independent datasets. In this paper, we offer to operate the advanced arrangement of deep learning techniques for multidomain and multitask approach in ABSA. The main tasks in ABSA: aspect extraction, category identification, sentiment classification and domain classification are all finished by an integration framework of Convolutional Neural Network (CNN), Bidirectional Independent Long Short Term Memory (BiIndyLSTM) and Attention mechanism. In addition, we use a POS tag layer combined with GloVe in word embedding layer to get the morphological attributes of each token word from review sentences. Through the experimenting process in the Laptop_Restaurant_Hotel multidomain dataset, we found that our proposed model has achieved high precision in multitasking ABSA. With this approach, we hope our proposed model will lay the foundation for ensuring flexibility and multiutility compared to previous opinion analysis models.